DET Dataset

The DET (Detection) dataset, frequently used in evaluating object detection models, particularly focuses on addressing the challenges of detecting small objects, especially in aerial imagery from UAVs. Current research emphasizes improving the accuracy and efficiency of object detection, particularly for small and densely packed objects, using various architectures like YOLOv5 and transformer-based models, often incorporating techniques such as attention mechanisms, improved feature extraction (e.g., Spatial Pyramid Pooling), and refined sampling strategies. This research is significant for advancing computer vision capabilities in applications such as autonomous driving, surveillance, and drone-based monitoring, where accurate and real-time object detection is crucial.